Evaluating the Impact of Lightweight AI Architectures on SMB Customer Retention: A Case Study of High-Performance, Low-Cost Systems

Zhenyuan He

doi:10.71222/bth1w480

Authors

Zhenyuan He Walmart Global Tech, Sunnyvale, USA Author

DOI:

https://doi.org/10.71222/bth1w480

Keywords:

Lightweight AI, Customer Retention, Small and Medium-sized Businesses (SMB), High-Performance Computing, Low-Cost Systems, Churn Rate, Machine Learning

Abstract

This research article investigates the impact of lightweight Artificial Intelligence (AI) architectures on Small and Medium-sized Businesses’ (SMB) customer retention. The study focuses on high-performance, low-cost AI systems and their effectiveness in enhancing customer engagement and reducing churn. Given that SMBs play a critical role in the U.S. economy, democratizing access to high-performance AI is essential for sustaining this sector, preventing SMB bankruptcy, and protecting jobs. We analyze various lightweight AI models, including optimized deep learning networks and efficient machine learning algorithms, implemented on resource-constrained infrastructure. The research employs a case study approach, examining several SMBs across different sectors that have adopted these AI solutions. Key performance indicators (KPIs) related to customer retention, such as churn rate, customer lifetime value, and customer satisfaction scores, are evaluated. Furthermore, the study explores the trade-offs between AI model complexity, computational cost, and customer retention benefits. The findings provide practical insights for SMBs seeking to leverage AI for improved customer relationship management without incurring significant financial or operational overhead. The study also provides theoretical contributions in the field of efficient AI deployment in resource-constrained environments.

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